Gene Expression Omnibus Datasets Research Articles

Decreased TLR7 expression was associated with airway eosinophilic inflammation and lung function in asthma: evidence from machine learning approaches and experimental validation

BackgroundAsthma is a global public health concern. The underlying pathogenetic mechanisms of asthma were poorly understood. This study aims to explore potential biomarkers associated with asthma and analyze the pathological role of immune cell infiltration in the disease.MethodsThe gene expression profiles of induced sputum were obtained from Gene Expression Omnibus datasets (GSE76262 and GSE137268) and were combined for analysis. Toll-like receptor 7 (TLR7) was identified as the core gene by the intersection of two different machine learning algorithms, namely, least absolute shrinkage and selector operation (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE), and the top 10 core networks based on Cytohubba. CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between asthma and healthy control groups. Finally, the expression level of TLR7 was validated in induced sputum samples of patients with asthma.ResultsA total of 320 differential expression genes between the asthma and healthy control groups were screened, including 184 upregulated genes and 136 downregulated genes. TLR7 was identified as the core gene after combining the results of LASSO regression, SVM-RFE algorithm, and top 10 hub genes. Significant differences were observed in the distribution of 13 out of 22 infiltrating immune cells in asthma. TLR7 was found to be closely related to the level of several infiltrating immune cells. TLR7 mRNA levels were downregulated in asthmatic patients compared with healthy controls (p = 0.0049). The area under the curve of TLR7 for the diagnosis of asthma was 0.7674 (95% CI 0.631–0.904, p = 0.006). Moreover, TLR7 mRNA levels were negatively correlated with exhaled nitric oxide fraction (r = − 0.3268, p = 0.0347) and the percentage of peripheral blood eosinophils (%) (r = − 0.3472, p = 0.041), and positively correlated with forced expiratory volume in the first second (FEV1) (% predicted) (r = 0.3960, p = 0.0071) and FEV1/forced vital capacity (r = 0.3213, p = 0.0314) in asthmatic patients.ConclusionsDecreased TLR7 in the induced sputum of eosinophilic asthmatic patients was involved in immune cell infiltration and airway inflammation, which may serve as a new biomarker for the diagnosis of eosinophilic asthma.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes.

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC. Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes. HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified. This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

Systematic analysis of MASP-1 serves as a novel immune-related biomarker in sepsis and trauma followed by preliminary experimental validation.

Dysregulated immune response in trauma and sepsis leads to the abnormal activation of the complement and coagulation systems. Mannose-binding lectin (MBL)-associated serine protease-1 (MASP-1) activates the lectin pathway of the complement system and mediates proinflammatory and procoagulant reactions. However, the potential effects of MASP-1 in trauma and sepsis have not yet been explored. We obtained five sepsis, two trauma, and one sepsis and trauma RNA-sequencing dataset from the Gene Expression Omnibus (GEO) database and conducted a comprehensive evaluation of the expression pattern, biological functions, and diagnostic value of MASP-1 in trauma and sepsis. Additionally, we investigated the association between MASP-1 expression and clinicopathological characteristics of trauma and sepsis. Furthermore, we collected clinical specimens to preliminarily validate the expression level and diagnostic efficacy of MASP-1 as well as the correlation of MASP-1 with clinical features of trauma and sepsis. Subsequently, we conducted a correlation analysis among MASP-1, immune cell infiltration, and immune and molecular pathways. Finally, we mechanistically analyzed the relationship among MASP-1, specific immune cells, and pivotal molecular pathways. MASP-1 expression was significantly upregulated in the trauma/sepsis samples compared to the control samples in the GEO datasets. MASP-1 exhibited excellent diagnostic values (AUC > 0.7) in multiple datasets and at multiple time points and could efficiently distinguish trauma/sepsis samples from the control samples. Moreover, MASP-1 expression was significantly positively correlated with the severity of the disease (APACHE-II, CRP, and neutrophil levels). These results were further validated by real-time quantitative polymerase chain reaction and enzyme-linked immunosorbent assay. Functional enrichment analysis revealed that MASP-1 primarily promotes trauma and sepsis via the immune-related signaling pathway. MASP-1 was significantly correlated with the infiltration of specific immune cells (such as B cells, CD8 T cells, neutrophils, macrophages, and infiltrating lymphocytes) and immune and molecular pathways (such as checkpoint, HLA, IL6/JAK/STAT3 signaling, necrosis, T-cell co-inhibition, and T-cell co-stimulation). Finally, analysis of the transcription and single-cell data revealed that MASP-1 was specifically expressed in T cells, and further correlation analysis revealed a close correlation between MASP-1 expression, proportion of CD8 T cells, and IL6/JAK/STAT3 signaling scores. Our results suggest that MASP-1 can serve as an immune-related biomarker for the diagnosis and disease severity of trauma and sepsis. It may activate the IL6 JAK-STAT3 signaling pathway and promote CD8 T-cell depletion to trigger traumatic sepsis.

Bioinformatics-based identification of key candidate genes and signaling pathways in patients with Parkinson's disease and obstructive sleep apnea.

Existing evidence exhibits that obstructive sleep apnea (OSA) is a potential consequence of Parkinson's disease (PD) or a contributor to PD progression. This investigation aimed to detect potential critical genes and molecular mechanisms underlying interactions between PD and OSA through bioinformatics analyses. The Gene Expression Omnibus (GEO) database was employed to obtain the expression profiles GSE20163 and GSE135917. The identification of common genes connected to PD and OSA was performed utilizing weighted gene co-expression network analysis and the R 4.0.4 program. The Cytoscape program was utilized to generate a network of protein-protein interactions (PPI), and the CytoHubba plugin was utilized to detect hub genes. Subsequently, functional enrichment analyses of the hub genes were conducted. Markers with increased diagnostic values for PD and OSA were confirmed using the GEO datasets GSE8397 and GSE38792. Typically, 57 genes that are common were identified in PD and OSA. Among these common genes, the top 10 hub genes in the PPI network were chosen. The verified datasets confirmed the presence of three important genes: CADPS, CHGA, and SCG3. Functional enrichment analysis revealed that these hub genes mostly participate in GABAergic synapses. Our findings suggest that CADPS, CHGA, and SCG3 are key genes involved in molecular mechanisms underlying interactions between OSA and PD. Functional enrichment of hub genes indicated a link between GABAergic synapses and the shared pathogenesis of PD and OSA. These candidate genes and corresponding pathways offer novel insights regarding biological targets that underlie the transcriptional connection between OSA and PD.

Dysregulated lipid metabolism is associated with kidney allograft fibrosis

BackgroundInterstitial fibrosis and tubular atrophy (IF/TA), a histologic feature of kidney allograft destruction, is linked to decreased allograft survival. The role of lipid metabolism is well-acknowledged in the area of chronic kidney diseases; however, its role in kidney allograft fibrosis is still unclarified. In this study, how lipid metabolism contributes to kidney allografts fibrosis was examined.MethodsA comprehensive bioinformatic comparison between IF/TA and normal kidney allograft in the Gene Expression Omnibus (GEO) database was conducted. Further validations through transcriptome profiling or pathological staining of human recipient biopsy samples and in rat models of kidney transplantation were performed. Additionally, the effects of enhanced lipid metabolism on changes in the fibrotic phenotype induced by TGF-β1 were examined in HK-2 cell.ResultsIn-depth analysis of the GEO dataset revealed a notable downregulation of lipid metabolism pathways in human kidney allografts with IF/TA. This decrease was associated with increased level of allograft rejection, inflammatory responses, and epithelial mesenchymal transition (EMT). Pathway enrichment analysis showed the downregulation in mitochondrial LC-fatty acid beta-oxidation, fatty acid beta-oxidation (FAO), and fatty acid biosynthesis. Dysregulated fatty acid metabolism was also observed in biopsy samples from human kidney transplants and in fibrotic rat kidney allografts. Notably, the areas affected by IF/TA had increased immune cell infiltration, during which increased EMT biomarkers and reduced CPT1A expression, a key FAO enzyme, were shown by immunohistochemistry. Moreover, under TGF-β1 induction, activating CPT1A with the compound C75 effectively inhibited migration and EMT process in HK-2 cells.ConclusionsThis study reveal a critical correlation between dysregulated lipid metabolism and kidney allograft fibrosis. Enhancing lipid metabolism with CPT1A agonists could be a therapeutic approach to mitigate kidney allografts fibrosis.

The gene expression of CALD1, CDH2, and POSTN in fibroblast are related to idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive lung dysfunction due to excessive collagen production and tissue scarring. Despite recent advancements, the molecular mechanisms remain unclear. RNA sequencing identified 475 differentially expressed genes (DEGs) in the TGF-β1-induced primary lung fibrosis model. Gene expression chips GSE101286 and GSE110147 from NCBI gene expression omnibus (GEO) database were analyzed using GEO2R, revealing 94 DEGs in IPF lung tissue samples. The gene ontology (GO) and pathway enrichment, Protein-protein interaction (PPI) network construction, and Maximal Clique Centrality (MCC) scoring were performed. Experimental validation included RT-qPCR, Immunohistochemistry (IHC), and Western Blot, with siRNA used for gene knockdown. A co-expression network was constructed by GeneMANIA. GO enrichment highlighted significant enrichment of DEGs in TGF-β cellular response, connective tissue development, extracellular matrix components, and signaling pathways such as the AGE-RAGE signaling pathway and ECM-receptor interaction. PPI network analysis identified hub genes, including FN1, COL1A1, POSTN, KIF11, and ECT2. CALD1 (Caldesmon 1), CDH2 (Cadherin 2), and POSTN (Periostin) were identified as dysregulated hub genes in both the RNA sequencing and GEO datasets. Validation experiments confirmed the upregulation of CALD1, CDH2, and POSTN in TGF-β1-treated fibroblasts and IPF lung tissue samples. IHC experiments probed tissue-level expression patterns of these three molecules. Knockdown of CALD1, CDH2, and POSTN attenuated the expression of fibrotic markers (collagen I and α-SMA) in response to TGF-β1 stimulation in primary fibroblasts. Co-expression analysis revealed interactions between hub genes and predicted genes involved in actin cytoskeleton regulation and cell-cell junction organization. CALD1, CDH2, and POSTN, identified as potential contributors to pulmonary fibrosis, present promising therapeutic targets for IPF patients.

Circ_0039787 promotes cervical cancer cell tumorigenesis by regulation of the miR-877-5p-KRAS axis.

Circular RNA (circRNA) is a novel type of RNA that plays an important role in the occurrence and development of many malignant tumors. However, the potential regulatory role and molecular mechanisms of circRNAs in cervical cancer (CC) are still not clear. Here, we explored circRNAs associated with CC from the Gene Expression Omnibus (GEO) datasets GSE113696 and GSE102686. We initially identified circ_0039787, which is derived from exons 2 to 3 of the C16orf70 gene. We observed that circ_0039787 is mainly located in the cytoplasm and is more stable than its linear counterpart, C16orf70. circ_0039787 is significantly upregulated in CC tissues and cells. In addition, functional gain and loss experiments demonstrated that circ_0039787 promotes the proliferation, migration, and invasion of CC cells in vitro and the growth of CC tumors in vivo. Mechanistically, circ_0039787 promotes CC tumor progression by competitively absorbing miR-877-5p to alleviate the inhibitory effect of miR-877-5p on Kirsten Rat Sarcoma viral oncogene homolog (KRAS) expression. Overall, our results suggest that circ_0039787 could serve as a promising diagnostic biomarker and potential therapeutic target for CC patients.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Disulfidptosis-related genes of prognostic signature and immune infiltration features in hepatocellular carcinoma supported by bulk and single-cell RNA sequencing data.

Disulfidptosis is a new type of cellular death triggered in response to disulfide stress and is strongly linked to the progression of malignancies. Hepatocellular carcinoma (HCC) is a very common malignancy. Some reports have suggested a link between disulfidptosis-related genes (DRGs) and cancer; however, further research needs to be conducted. In this study, HCC data from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma and Gene Expression Omnibus data sets were collected and analyzed. A univariate Cox regression analysis, least absolute shrinkage and selection operator, and multivariate Cox regression analysis were conducted to identify the hub DRGs signature for prognosis. The HCC patients were allocated to high- and low-risk groups based on their disulfidptosis risk scores. The model was validated with a high degree of precision using both internal and external validation data sets. "ESTIMATE" and "CIBERSORT" packages were employed to assess the immunological landscapes and immune cell infiltration. The IMvigor210 cohort was chosen to validate the immunotherapy results. A drug sensitivity analysis was conducted to identify targeted medications. The expression of the hub DRGs in the HCC cells was confirmed using cytological techniques. The bioinformatic analysis revealed that 16 genes showed differential expression. A prognostic model was developed based on four genes: RPN1, SLC2A1, SLC2A4, and SLC7A11. A notable difference in prognosis was observed between the two risk groups. Based on the results of the immune microenvironment, tumor mutation burden, immunotherapy, and drug screening analyses, the DRGs signature can be employed in HCC immunotherapy decision making. Further, the expression levels of the hub DRGs were significantly upregulated in the HCC cells. Our four-DRGs signature could be used to predict HCC prognosis. Further, this study showed that the hub DRGs could serve as biomarkers for immunotherapy prediction and could potentially guide targeted therapies.

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma.

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.

Identification of differentially expressed genes associated with ferroptosis in Crohn's disease.

Ferroptosis-related genes may play a critical regulatory role in the pathogenesis of Crohn's disease (CD). The purpose of the present study was to identify genes expressed in CD that are associated with ferroptosis, and to provide guidance in the diagnosis and therapy of CD. CD mRNA expression data were initially gathered from the Gene Expression Omnibus (GEO) database. GSE75214 and GSE102133 datasets were selected as the major targets and were analyzed for differentially expressed genes (DEGs). Subsequently, R software was used to analyze the common genes among the DEGs between CD and ferroptosis-related genes. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genome pathway analysis were conducted to identify related pathways and functions. Protein-protein interaction (PPI) analysis was performed to identify target genes. The DSigDB website was used to predict potential target drugs for hub genes. Reverse transcription-quantitative (RT-q) PCR was employed to detect the expression of these ferroptosis-related genes in clinical samples obtained from healthy controls and patients with CD. According to the two GEO datasets, 13 ferroptosis DEGs (11 upregulated genes and two downregulated genes) were identified in CD with thresholds of P<0.05 and |log2 fold change|>1, and were selected for further analysis. PPI analysis indicated the mutual effects among these genes and filtered out five hub genes. The top 10 potential targeted drugs were selected. The qPCR results showed that the expression levels of three genes, namely, IL-6, prostaglandin-endoperoxide synthase 2 (PTGS2) and dual oxidase 2 (DUOX2), were different between CD samples and healthy samples. This result was consistent with the results obtained from the bioinformatics analysis. In conclusion, bioinformatics analysis identified a total of 13 ferroptosis-associated genes in CD. Further verification by qPCR showed that IL-6, PTGS2 and DUOX2 may affect the process of CD by regulating ferroptosis. These findings might provide new biomarkers, diagnostic and therapeutic markers for CD.

LINC01977 promotes colorectal cancer growth and metastasis by enhancing aerobic glycolysis via the ERK/c-Myc axis.

How colorectal cancer (CRC) gain the ability to growth and metastasis remains largely unknown. Findings from preceding studies have revealed the participation of long non-coding RNAs (lncRNAs) in CRC progression. However, the role of LINC01977 in CRC remains unexplored. This study aims to explore the function and underlying mechanism of LINC01977 in CRC. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the expression of LINC01977 in CRC and its correlation with CRC prognosis. In our research, we explored the influence of LINC01977 on CRC progression such as cell proliferation, migration, invasion, and aerobic glycolysis, and identified its fundamental molecular mechanism using in vitro CRC cell lines and in vivo mouse xenograft models. LINC01977 exhibited significantly elevated expression in CRC tissues and cell lines, and its level was significantly correlated with malignant clinicopathological characteristics and negative prognosis. Furthermore, both in vivo and in vitro tests revealed LINC01977's role in facilitating CRC cell proliferation and metastasis. LINC01977's significant part in CRC aerobic glycolysis was also discovered. With an aim to uncover the underlying mechanism, we investigated LINC01977's effect on c-Myc, a key gene in glycolysis. The results showed that LINC01977 regulated c-Myc stability via extracellular signal-regulated kinase (ERK)-mediated phosphorylation, and LINC01977-mediated c-Myc activated the level of vital glycolysis-related genes such as HK2, PGK1, LDHA, and GLUT1. Rescue experiments further confirmed that LINC01977 promoted CRC proliferation, metastasis, and aerobic glycolysis via c-Myc. This study is the first to report that LINC01977 facilitates CRC proliferation, metastasis, and aerobic glycolysis through c-Myc, suggesting its potential as a therapeutic target for CRC treatment.

Lipid metabolism-related genes are involved in the occurrence of asthma and regulate the immune microenvironment

BackgroundLipid metabolism plays a pivotal role in asthma pathogenesis. However, a comprehensive analysis of the importance of lipid metabolism-related genes (LMRGs) in regulating the immune microenvironment in asthma remains lacking. The transcriptome matrix was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed analysis and weighted gene coexpression network analysis (WGCNA) were conducted on the GSE74986 dataset to select hub LMRGs, and gene set enrichment analysis (GSEA) was conducted to explore their biological functions. The CIBERSORT algorithm was used to determine immune infiltration in the asthma and control groups, and the correlation of diagnostic biomarkers and immune cells was performed via Spearman correlation analysis. Subsequently, a competitive endogenous RNA (ceRNA) network was constructed to investigate the hidden molecular mechanism of asthma. The expression levels of the hub genes were further validated in the GSE143192 dataset, and RT‒qPCR and immunofluorescence were performed to verify the reliability of the results in the OVA asthma model. Lastly, the ceRNA network was confirmed by qRT-PCR and RNAi experiments in the characteristic cytokine (IL-13)-induced asthma cellular model.ResultsASAH1, ACER3 and SGPP1 were identified as hub LMRGs and were mainly involved in protein secretion, mTORC1 signaling, and fatty acid metabolism. We found more infiltration of CD8+ T cells, activated NK cells, and monocytes and less M0 macrophage infiltration in the asthma group than in the healthy control group. In addition, ASAH1, ACER3, and SGPP1 were negatively correlated with CD8+ T cells and activated NK cells, but positively correlated with M0 macrophages. Within the ceRNA network, SNHG9-hsa-miR-615-3p-ACER3, hsa-miR-212-5p and hsa-miR-5682 may play crucial roles in asthma pathogenesis. The low expression of ASAH1 and SGPP1 in asthma was also validated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upregulated, while that of ACER3 was significantly downregulated.ConclusionASAH1, ACER3 and SGPP1 might be diagnostic biomarkers for asthma, and are associated with increased immune system activation. In addition, SNHG9-hsa-miR-615-3p-ACER3 may be viewed as effective therapeutic targets for asthma. Our findings might provide a novel perspective for future research on asthma.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer.

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC. We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

TRPM8 inhibits substance P release from primary sensory neurons via PKA/GSK-3beta to protect colonic epithelium in colitis

Transient receptor potential melastatin 8 (TRPM8) is a cold sensory receptor in primary sensory neurons that regulates various neuronal functions. Substance P (SP) is a pro-inflammatory neuropeptide secreted by the neurons, and it aggravates colitis. However, the regulatory role of TRPM8 in SP release is still unclear. Our study aimed to investigate TRPM8’s role in SP release from primary sensory neurons during colitis and clarify the effect of SP on colonic epithelium. We analyzed inflammatory bowel disease patients’ data from the Gene Expression Omnibus dataset. Dextran sulfate sodium (DSS, 2.5%)-induced colitis in mice, mouse dorsal root ganglion (DRG) neurons, ND7/23 cell line, and mouse or human colonic organoids were used for this experiment. Our study found that TRPM8, TAC1 and WNT3A expression were significantly correlated with the severity of ulcerative colitis in patients and DSS-induced colitis in mice. The TRPM8 agonist (menthol) and the SP receptor antagonist (Aprepitant) can attenuate colitis in mice, but the effects were not additive. Menthol promoted calcium ion influx in mouse DRG neurons and inhibited the combination and phosphorylation of PKAca from the cAMP signaling pathway and GSK-3β from the Wnt/β-catenin signaling pathway, thereby inhibiting the effect of Wnt3a-driven β-catenin on promoting SP release in ND7/23 cells. Long-term stimulation with SP inhibited proliferation and enhanced apoptosis in both mouse and human colonic organoids. Conclusively, TRPM8 inhibits SP release from primary sensory neurons by inhibiting the interaction between PKAca and GSK-3β, thereby inhibiting the role of SP in promoting colonic epithelial apoptosis and relieving colitis.

DNAJC12 causes breast cancer chemotherapy resistance by repressing doxorubicin-induced ferroptosis and apoptosis via activation of AKT

BackgroundChemotherapy is a primary treatment for breast cancer (BC), yet many patients develop resistance over time. This study aims to identify critical factors contributing to chemoresistance and their underlying molecular mechanisms, with a focus on reversing this resistance. MethodsWe utilized samples from the Gene Expression Omnibus (GEO) and West China Hospital to identify and validate genes associated with chemoresistance. Functional studies were conducted using MDA-MB-231 and MCF-7 cell lines, involving gain-of-function and loss-of-function approaches. RNA sequencing (RNA-seq) identified potential mechanisms. We examined interactions between DNAJC12, HSP70, and AKT using co-immunoprecipitation (Co-IP) assays and established cell line-derived xenograft (CDX) models for in vivo validations. ResultsBoruta analysis of four GEO datasets identified DNAJC12 as highly significant. Patients with high DNAJC12 expression showed an 8 % pathological complete response (pCR) rate, compared to 38 % in the low expression group. DNAJC12 inhibited doxorubicin (DOX)-induced cell death through both ferroptosis and apoptosis. Combining apoptosis and ferroptosis inhibitors completely reversed DOX resistance caused by DNAJC12 overexpression. RNA-seq suggested that DNAJC12 overexpression activated the PI3K-AKT pathway. Inhibition of AKT reversed the DOX resistance induced by DNAJC12, including reduced apoptosis and ferroptosis, restoration of cleaved caspase 3, and decreased GPX4 and SLC7A11 levels. Additionally, DNAJC12 was found to increase AKT phosphorylation in an HSP70-dependent manner, and inhibiting HSP70 also reversed the DOX resistance. In vivo studies confirmed that AKT inhibition reversed DNAJC12-induced DOX resistance in the CDX model. ConclusionDNAJC12 expression is closely linked to chemoresistance in BC. The DNAJC12-HSP70-AKT signaling axis is crucial in mediating resistance to chemotherapy by suppressing DOX-induced ferroptosis and apoptosis. Our findings suggest that targeting AKT and HSP70 activities may offer new therapeutic strategies to overcome chemoresistance in BC.

Expression levels and clinical significance of ferroptosis-related genes in patients with myocardial infarction

Myocardial infarction (MI) is the most serious type of cardiovascular disease and the leading cause of cardiac death.Ferroptosis is one of the newly discovered programmed cell death modes in MI, but its mechanism of action in MI has not been clarified.In this study, we analyzed the expression changes of ferroptosis-related genes in MI and explored the potential mechanisms of ferroptosis-related functions in myocardial infarction. Public data sets GSE19339, GSE97320 and GSE141512 were retrieved from the Gene Expression Omnibus (GEO) Datasets public database. After data preprocessing, differentially expressed genes were screened, and differentially expressed ferroptosis-related genes associated with myocardial infarction were obtained. The biological function and signaling pathway enrichment analysis were performed to establish the PPI interaction network specific to heart tissue, and the differential diagnosis significance of differentially expressed ferroptosis-related genes associated with myocardial infarction was analyzed by ROC curve and decision tree model.A total of 317 genes showed significant changes in expression levels in patients with myocardial infarction, including 205 down-regulated genes and 112 up-regulated genes.Gene Ontology (GO) enrichment analysis and functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways showed that these genes were mainly involved in signaling pathways or biological functions related to inflammation and apoptosis.Five differentially expressed ferroptosis-related genes (SLC2A3, EPAS1, HMOX1, ATM, FANCD2) were obtained, all of which played key biological functions in cardiac tissue function. SLC2A3, EPAS1, HMOX1, ATM and FANCD2 genes all had good diagnostic value for myocardial infarction (P < 0.05). The increase of SLC2A3, EPAS1 and HMOX1 are risk factors for myocardial infarction, while ATM and FANCD2 are protective factors.Decision tree analysis showed that SLC2A3, HMOX1, ATM, FANCD2 gene had higher net yield in diagnosing myocardial infarction. In summary, the mechanism of ferroptosis is involved in the occurrence and progression of myocardial infarction. In this study, five differentially expressed ferroptosis-related genes associated with myocardial infarction were retrieved, which may be good biomarkers of ferroptosis after MI.These findings also suggest that the differential expression of ferroptosis-related genes associated with myocardial infarction has significant diagnostic significance for myocardial infarction.

TRIP13 Enhances Radioresistance of Lung Adenocarcinoma Cells through the Homologous Recombination Pathway

Radiation therapy is one of the most common treatments for non-small cell lung cancer (NSCLC). However, the insensitivity of some tumor cells to radiation is one of the major reasons for the poor efficacy of radiotherapy and the poor prognosis of patients, and exploring the underlying mechanisms behind radioresistance is the key to solving this clinical challenge. This study aimed to identify the molecules associated with radioresistance in lung adenocarcinoma (LUAD), identified thyroid hormone receptor interactor 13 (TRIP13) as the main target initially, and explored whether TRIP13 is related to radioresistance in LUAD and the specific mechanism, with the aim of providing theoretical basis and potential targets for the combination therapy of LUAD patients receiving radiotherapy in the clinic. Three datasets, GSE18842, GSE19188 and GSE33532, were selected from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (|log FC|>1.5, P<0.05) in each of the three datasets using the R 4.1.3 software, and then Venn diagram was used to find out the differentially expressed genes common to the three datasets. The screened differential genes were then subjected to protein-protein interaction (PPI) analysis and module analysis with the help of STRING online tool and Cytoscape software, and survival prognosis analysis was performed for each gene with the help of Kaplan-Meier Plotter database, and the TRIP13 gene was identified as the main molecule for subsequent studies. Subsequently, the human LUAD cell line H292 was irradiated with multiple X-rays using a sub-lethal dose irradiation method to construct a radioresistant cell line, H292DR. The radioresistance of H292DR cells was verified using cell counting kit-8 (CCK-8) assay and clone formation assay. The expression levels of TRIP13 in H292 and H292DR cells were measured by Western blot. Small interfering RNA (siRNA) was used to silence the expression of TRIP13 in H292DR cells and Western blot assay was performed. The clone formation ability and migration ability of H292DR cells were observed after TRIP13 silencing, followed by the detection of changes in the expression levels of proteins closely related to homologous recombination, such as ataxia telangiectasia mutated (ATM) protein. Screening of multiple GEO datasets, validation of external datasets and survival analysis revealed that TRIP13 was highly expressed in LUAD and was associated with poor prognosis in LUAD patients who had received radiation therapy. And the results of gene set enrichment analysis (GSEA) of TRIP13 suggested that TRIP13 might be closely associated with LUAD radioresistance by promoting homologous recombination repair after radiation therapy. Experimentally, TRIP13 expression was found to be upregulated in H292DR, and silencing of TRIP13 was able to increase the sensitivity of H292DR cells to radiation. TRIP13 is associated with poor prognosis in LUAD patients treated with radiation, possibly by promoting a homologous recombination repair pathway to mediate resistance of LUAD cells to radiation.

Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning

BackgroundSystemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level.MethodsThe gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier.ResultsWe finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model’s performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model.ConclusionsWe successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.